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Record W4400899116 · doi:10.1051/e3sconf/202455201136

Sustainable Power Flow: Voltage Distribution Strategies for Renewable Energy Integration

2024· article· en· W4400899116 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueE3S Web of Conferences · 2024
Typearticle
Languageen
FieldEngineering
TopicIslanding Detection in Power Systems
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsRenewable energyInterfacingDistributed generationVoltageComputer scienceLow voltageMATLABWind powerVoltage droopFault (geology)Reliability engineeringControl engineeringAutomotive engineeringElectrical engineeringEngineeringVoltage regulator

Abstract

fetched live from OpenAlex

The rapid expansion of green energy resources (RER) into existing electrical networks necessitates an evolved approach to voltage distribution. This study explores the challenges and solutions associated with integrating green energy into high and low voltage distribution systems (HVDS and LVDS). The research evaluates various protection schemes for dynamic fault currents, voltage control systems for mitigating power quality issues, and optimal planning strategies for distributed generation. Innovative methodologies for integrating solar and wind energy, such as centralized-decentralized control approaches and demand response mechanisms, are proposed. The study demonstrates, through MATLAB simulations, that HVDS configurations significantly improve system efficiency and reduce technical losses compared to LVDS, particularly when interfacing with green energy sources.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.448

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.223
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it